SoroushVahidi/scidocs huggingface dataset:
Energy-Aware Genetic Algorithms for Task Scheduling in Cloud Computing
(2012)

For the cloud computing, task scheduling problems are of paramount importance. It becomes more challenging when takes into account energy consumption, traditional make span criteria and users QoS as objectives. This paper considers independent tasks scheduling in cloud computing as a bi-objective minimization problem with make span and energy consumption as the scheduling criteria. We use Dynamic Voltage Scaling (DVS) to minimize energy consumption and propose two algorithms. These two algorithms use the methods of unify and double fitness to define the fitness function and select individuals. They adopt the genetic algorithm to parallel find the reasonable scheduling scheme. The simulation results demonstrate the two algorithms can efficiently find the right compromise between make span and energy consumption.
A* Parsing: Fast Exact Viterbi Parse Selection
(2003)

A PCFGparsingcandramaticallyreducethetime requiredto find theexactViterbi parseby conservatively estimatingoutsideViterbi probabilities.Wediscussvariousestimatesandgive efficientalgorithmsfor computingthem.On Penntreebanksentences, ourmostdetailedestimate reducesthetotalnumberof edgesprocessedto lessthan3%of thatrequiredby exhausti ve parsing,andevenasimplerestimatewhichcanbepre-computedin underaminutestill reduces thework by a factorof 5. ThealgorithmextendstheclassicA graphsearchprocedureto a certainhypergraphassociatedwith parsing.Unlike best-firstandfinite-beammethodsfor achieving thiskind of speed-up, theA parseris guaranteedto returnthemostlikely parse,not justanapproximation.Thealgorithmis alsocorrectfor awide rangeof parsercontrolstrategies andmaintainsaworst-casecubictime bound. A Parsing: FastExact Viterbi ParseSelection
Statistical language learning
(1993)

The $64,000 question in computational linguistics these days is: “What should I read to learn about statistical natural language processing?” I have been asked this question over and over, and each time I have given basically the same reply: there is no text that addresses this topic directly, and the best one can do is find a good probability-theory textbook and a good information-theory textbook, and supplement those texts with an assortment of conference papers and journal articles. Understanding the disappointment this answer provoked, I was delighted to hear that someone had finally written a bookdirectly addressing this topic. However, after reading Eugene Charniak’s Statistical Language Learning, I have very mixed feelings about the impact this bookmight have on the ever-growing field of statistical NLP. The book begins with a very brief description of the classic artificial intelligence approach toNLP (chapter 1), including morphology, syntax, semantics, and pragmatics. It presents a few definitions from probability theory and information theory (chapter 2), then proceeds to introduce hidden Markov models (chapters 3–4) and probabilistic context-free grammars (chapters 5–6). The book concludes with a few chapters discussing advanced topics in statistical language learning, such as grammar induction (chapter 7), syntactic disambiguation (chapter 8), word clustering (chapter 9), andword sense disambiguation (chapter 10). To its credit, the book serves as an interesting popular discussion of statistical modeling in NLP. It is well-written and entertaining, and very accessible to the reader with a limited mathematical background. It presents a good selection of statistical NLP topics to introduce the reader to the field. And the descriptions of the forward-backward algorithm for hidden Markov models and the inside-outside algorithm for probabilistic context-free grammars are intuitive and easy to follow. However, as a resource for someone interested in entering this area of research, this book falls far short of its author’s goals. These goals are clearly stated in the preface:
The PASCAL Recognising Textual Entailment Challenge
(2005)

This paper describes the PASCAL Network of Excellence first Recognising Textual Entailment (RTE-1) Challenge benchmark. The RTE task is defined as recognizing, given two text fragments, whether the meaning of one text can be inferred (entailed) from the other. This application-independent task is suggested as capturing major inferences about the variability of semantic expression which are commonly needed across multiple applications. The Challenge has raised noticeable attention in the research community, attracting 17 submissions from diverse groups, suggesting the generic relevance of the task.
Adaptive duplicate detection using learnable string similarity measures
(2003)

The problem of identifying approximately duplicate records in databases is an essential step for data cleaning and data integration processes. Most existing approaches have relied on generic or manually tuned distance metrics for estimating the similarity of potential duplicates. In this paper, we present a framework for improving duplicate detection using trainable measures of textual similarity. We propose to employ learnable text distance functions for each database field, and show that such measures are capable of adapting to the specific notion of similarity that is appropriate for the field's domain. We present two learnable text similarity measures suitable for this task: an extended variant of learnable string edit distance, and a novel vector-space based measure that employs a Support Vector Machine (SVM) for training. Experimental results on a range of datasets show that our framework can improve duplicate detection accuracy over traditional techniques.
Scaling Textual Inference to the Web
(2008)

Most Web-based Q/A systems work by finding pages that contain an explicit answer to a question. These systems are helpless if the answer has to be inferred from multiple sentences, possibly on different pages. To solve this problem, we introduce the HOLMES system, which utilizes textual inference (TI) over tuples extracted from text. Whereas previous work on TI (e.g., the literature on textual entailment) has been applied to paragraph-sized texts, HOLMES utilizes knowledge-based model construction to scale TI to a corpus of 117 million Web pages. Given only a few minutes, HOLMES doubles recall for example queries in three disparate domains (geography, business, and nutrition). Importantly, HOLMES’s runtime is linear in the size of its input corpus due to a surprising property of many textual relations in the Web corpus—they are “approximately” functional in a well-defined sense.
Diversified Visual Attention Networks for Fine-Grained Object Classification
(2017)

Fine-grained object classification attracts increasing attention in multimedia applications. However, it is a quite challenging problem due to the subtle interclass difference and large intraclass variation. Recently, visual attention models have been applied to automatically localize the discriminative regions of an image for better capturing critical difference, which have demonstrated promising performance. Unfortunately, without consideration of the diversity in attention process, most of existing attention models perform poorly in classifying fine-grained objects. In this paper, we propose a diversified visual attention network (DVAN) to address the problem of fine-grained object classification, which substantially relieves the dependency on strongly supervised information for learning to localize discriminative regions com-pared with attention-less models. More importantly, DVAN explicitly pursues the diversity of attention and is able to gather discriminative information to the maximal extent. Multiple attention canvases are generated to extract convolutional features for attention. An LSTM recurrent unit is employed to learn the attentiveness and discrimination of attention canvases. The proposed DVAN has the ability to attend the object from coarse to fine granularity, and a dynamic internal representation for classification is built up by incrementally combining the information from different locations and scales of the image. Extensive experiments conducted on CUB-2011, Stanford Dogs, and Stanford Cars datasets have demonstrated that the pro-posed DVAN achieves competitive performance compared to the state-of-the-art approaches, without using any prior knowledge, user interaction, or external resource in training and testing.
DB2 Design Advisor: Integrated Automatic Physical Database Design
(2004)

The DB2 Design Advisor in IBM® DB2® Universal DatabaseTM (DB2 UDB) Version 8.2 for Linux®, UNIX® and Windows® is a tool that, for a given workload, automatically recommends physical design features that are any subset of indexes, materialized query tables (also called materialized views), shared-nothing database partitionings, and multidimensional clustering of tables. Our work is the very first industrial-strength tool that covers the design of as many as four different features, a significant advance to existing tools, which support no more than just indexes and materialized views. Building such a tool is challenging, because of not only the large search space introduced by the interactions among features, but also the extensibility needed by the tool to support additional features in the future. We adopt a novel “hybrid” approach in the Design Advisor that allows us to take important interdependencies into account as well as to encapsulate design features as separate components to lower the reengineering cost. The Design Advisor also features a built-in module that automatically reduces the given workload, and therefore provides great scalability for the tool. Our experimental results demonstrate that our tool can quickly provide good physical design recommendations that satisfy users’ requirements.
Differential Privacy as a Mutual Information Constraint
(2016)

Differential privacy is a precise mathematical constraint meant to ensure privacy of individual pieces of information in a database even while queries are being answered about the aggregate. Intuitively, one must come to terms with what differential privacy does and does not guarantee. For example, the definition prevents a strong adversary who knows all but one entry in the database from further inferring about the last one. This strong adversary assumption can be overlooked, resulting in misinterpretation of the privacy guarantee of differential privacy. Herein we give an equivalent definition of privacy using mutual information that makes plain some of the subtleties of differential privacy. The mutual-information differential privacy is in fact sandwiched between ε-differential privacy and (ε,δ)-differential privacy in terms of its strength. In contrast to previous works using unconditional mutual information, differential privacy is fundamentally related to conditional mutual information, accompanied by a maximization over the database distribution. The conceptual advantage of using mutual information, aside from yielding a simpler and more intuitive definition of differential privacy, is that its properties are well understood. Several properties of differential privacy are easily verified for the mutual information alternative, such as composition theorems.
Large-signal-modulation of high-efficiency light-emitting diodes for optical communication
(2000)

The dynamic behavior of high-efficiency light-emitting diodes (LEDs) is investigated theoretically and experimentally. A detailed theoretical description of the switch-on and switch-off transients of LEDs is derived. In the limit of small-signal modulation, the well-established exponential behavior is obtained. However, in the case of high injection, which is easily reached for thin active layer LEDs, the small-signal time constant is found to be up to a factor of two faster than the radiative recombination lifetime. Using such quantum-well LEDs, we have demonstrated optical data transfer with wide open eye diagrams at bit rates up to 2 Gbit/s. In addition, we have combined the use of thin active layers with the concept of surface-textured thin-film LEDs, which allow a significant improvement in the light extraction efficiency. With LEDs operating at 0.5 Gbit/s and 1 Gbit/s, we have achieved external quantum efficiencies of 36% and 29%, respectively.
Highly Efficient Doherty Amplifier Based on Class-E Topology for WCDMA Applications
(2008)

This letter reports a high-efficiency gallium nitride (GaN) high-electron mobility transistor (HEMT) Doherty power amplifier (DPA) based on the class-E topology for wideband code-division multiple-access (WCDMA) applications. The class-E topology is employed as the carrier and peaking cells of the Doherty configuration. For validations, the proposed DPA is designed and implemented with 25-W GaN HEMTs at 2.14 GHz. For the proposed DPA, the power-added efficiency (PAE) and drain efficiency of 56.1% and 61.2% are achieved at 40 dBm (6-dB backoff power from Psat) for a continuous wave. For a 1-carrier WCDMA signal, the PAE of 44.8% is obtained with an adjacent channel leakage ratio (ACLR) of -31 dBc at 37 dBm, which is an 8.9% improvement over the conventional DPA with an ACLR of -36.4 dBc.
Botanical insecticides, deterrents, and repellents in modern agriculture and an increasingly regulated world.
(2006)

Botanical insecticides have long been touted as attractive alternatives to synthetic chemical insecticides for pest management because botanicals reputedly pose little threat to the environment or to human health. The body of scientific literature documenting bioactivity of plant derivatives to arthropod pests continues to expand, yet only a handful of botanicals are currently used in agriculture in the industrialized world, and there are few prospects for commercial development of new botanical products. Pyrethrum and neem are well established commercially, pesticides based on plant essential oils have recently entered the marketplace, and the use of rotenone appears to be waning. A number of plant substances have been considered for use as insect antifeedants or repellents, but apart from some natural mosquito repellents, little commercial success has ensued for plant substances that modify arthropod behavior. Several factors appear to limit the success of botanicals, most notably regulatory barriers and the availability of competing products (newer synthetics, fermentation products, microbials) that are cost-effective and relatively safe compared with their predecessors. In the context of agricultural pest management, botanical insecticides are best suited for use in organic food production in industrialized countries but can play a much greater role in the production and postharvest protection of food in developing countries.
Event-based Sensing for Space Situational Awareness
(2017)

A revolutionary type of imaging device, known as a silicon retina or event-based sensor, has recently been developed and is gaining in popularity in the field of artificial vision systems. These devices are inspired by a biological retina and operate in a significantly different way to traditional CCD-based imaging sensors. While a CCD produces frames of pixel intensities, an event-based sensor produces a continuous stream of events, each of which is generated when a pixel detects a change in log light intensity. These pixels operate asynchronously and independently, producing an event-based output with high temporal resolution. There are also no fixed exposure times, allowing these devices to offer a very high dynamic range independently for each pixel. Additionally, these devices offer high-speed, low power operation and a sparse spatiotemporal output. As a consequence, the data from these sensors must be interpreted in a significantly different way to traditional imaging sensors and this paper explores the advantages this technology provides for space imaging. The applicability and capabilities of event-based sensors for SSA applications are demonstrated through telescope field trials. Trial results have confirmed that the devices are capable of observing resident space objects from LEO through to GEO orbital regimes. Significantly, observations of RSOs were made during both day-time and nighttime (terminator) conditions without modification to the camera or optics. The event based sensor’s ability to image stars and satellites during day-time hours offers a dramatic capability increase for terrestrial optical sensors. This paper shows the field testing and validation of two different architectures of event-based imaging sensors. An eventbased sensor’s asynchronous output has an intrinsically low data-rate. In addition to low-bandwidth communications requirements, the low weight, low-power and high-speed make them ideally suitable to meeting the demanding challenges required by space-based SSA systems. Results from these experiments and the systems developed highlight the applicability of event-based sensors to ground and space-based SSA tasks.
Separable spectro-temporal Gabor filter bank features: Reducing the complexity of robust features for automatic speech recognition.
(2015)

To test if simultaneous spectral and temporal processing is required to extract robust features for automatic speech recognition (ASR), the robust spectro-temporal two-dimensional-Gabor filter bank (GBFB) front-end from Schädler, Meyer, and Kollmeier [J. Acoust. Soc. Am. 131, 4134-4151 (2012)] was de-composed into a spectral one-dimensional-Gabor filter bank and a temporal one-dimensional-Gabor filter bank. A feature set that is extracted with these separate spectral and temporal modulation filter banks was introduced, the separate Gabor filter bank (SGBFB) features, and evaluated on the CHiME (Computational Hearing in Multisource Environments) keywords-in-noise recognition task. From the perspective of robust ASR, the results showed that spectral and temporal processing can be performed independently and are not required to interact with each other. Using SGBFB features permitted the signal-to-noise ratio (SNR) to be lowered by 1.2 dB while still performing as well as the GBFB-based reference system, which corresponds to a relative improvement of the word error rate by 12.8%. Additionally, the real time factor of the spectro-temporal processing could be reduced by more than an order of magnitude. Compared to human listeners, the SNR needed to be 13 dB higher when using Mel-frequency cepstral coefficient features, 11 dB higher when using GBFB features, and 9 dB higher when using SGBFB features to achieve the same recognition performance.
Achievements : Re ̄ ections on the Measurement of Women ' s Empowerment
(1999)

This paper begins from the understanding that women's empowerment is about the process by which those who have been denied the ability to make strategic life choices acquire such an ability. Awide gap separates this processual understanding of empowerment from the more instrumentalist forms of advocacy which have required the measurement and quanti®cation of empowerment. The ability to exercise choice incorporates three inter-related dimensions: resources (de®ned broadly to include not only access, but also future claims, to both material and human and social resources); agency (including processes of decision making, as well as less measurable manifestations of agency such as negotiation, deception and manipulation); and achievements (well-being outcomes). A number of studies of women's empowerment are analysed to make some important methodological points about the measurement of empowerment. The paper argues that these three dimensions of choice are indivisible in determining the meaning of an indicator and hence its validity as a measure of empowerment. The notion of choice is further quali®ed by referring to the conditions of choice, its content and consequences. These quali®cations represent an attempt to incorporate the structural parameters of individual choice in the analysis of women's empowerment. CONCEPTUALIZING EMPOWERMENT
Low power LVDS transmitter with low common mode variation for 1GB/s-per pin operation
(2004)

This paper presents the design of a low voltage differential signalling (LVDS) transmitter intended using a LCD panel interface and is capable of transmitting 1GB/s per pin data rate. The transmitter is fully LVDS standard compatible which is achieved by employing a common mode feedback circuit to meet all Ac and DC specifications over PVT variation, specifically due to the implementation of feedback circuit, the common mode variation is made very less quiescent current of 5.5mA. Further, the implementation incorporates the idea of sharing of bias blocks among different transmitters in full chip resulting in less active area. The transmitter is implemented in 3.3V, 0.35/spl mu/ CMOS technology. In this implementation the active area (excluding ESD) per transmitter is 0.039 mm square. The initial silicon data (output offset voltage of 1.20V and differential voltage of 320mV) at room temperature shows the consistency of the simulation results.
MAGIC—Memristor-Aided Logic
(2014)

Memristors are passive components with a varying resistance that depends on the previous voltage applied across the device. While memristors are naturally used as memory, memristors can also be used for other applications, including logic circuits. In this brief, a memristor-only logic family, i.e., memristor-aided logic (MAGIC), is presented. In each MAGIC logic gate, memristors serve as an input with previously stored data, and an additional memristor serves as an output. The topology of a MAGIC nor gate is similar to the structure of a common memristor-based crossbar memory array. A MAGIC nor gate can therefore be placed within memory, providing opportunities for novel non-von Neumann computer architectures. Other MAGIC gates also exist (e.g., and, or, not, and nand) and are described in this brief.
Viewing Real-World Faces in 3D
(2013)

We present a data-driven method for estimating the 3D shapes of faces viewed in single, unconstrained photos (aka "in-the-wild"). Our method was designed with an emphasis on robustness and efficiency - with the explicit goal of deployment in real-world applications which reconstruct and display faces in 3D. Our key observation is that for many practical applications, warping the shape of a reference face to match the appearance of a query, is enough to produce realistic impressions of the query's 3D shape. Doing so, however, requires matching visual features between the (possibly very different) query and reference images, while ensuring that a plausible face shape is produced. To this end, we describe an optimization process which seeks to maximize the similarity of appearances and depths, jointly, to those of a reference model. We describe our system for monocular face shape reconstruction and present both qualitative and quantitative experiments, comparing our method against alternative systems, and demonstrating its capabilities. Finally, as a testament to its suitability for real-world applications, we offer an open, on-line implementation of our system, providing unique means of instant 3D viewing of faces appearing in web photos.